From 113925066712c0fa87e4e4c6aef74f776c6710f8 Mon Sep 17 00:00:00 2001 From: Su Min Park <92177904+michellesuminpark@users.noreply.github.com> Date: Thu, 21 Mar 2024 13:57:06 -0700 Subject: [PATCH 1/5] Created using Colaboratory --- admission_rate_part1.ipynb | 2068 ++++++++++++++++++++++++++++++++++++ 1 file changed, 2068 insertions(+) create mode 100644 admission_rate_part1.ipynb diff --git a/admission_rate_part1.ipynb b/admission_rate_part1.ipynb new file mode 100644 index 0000000..cdfa6d4 --- /dev/null +++ b/admission_rate_part1.ipynb @@ -0,0 +1,2068 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **[LS22] UC Berkeley Admission Rate**" + ], + "metadata": { + "id": "naFAkdOL7PXO" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "d5r5TaunzrFo" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "source": [ + "
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" + ], + "metadata": { + "id": "r9dIdDaj-Y0K" + } + }, + { + "cell_type": "markdown", + "source": [ + "# INSTRUCTOR ONLY: SPLIT 1 (1.2, 2.1, 2.2, 3.1, 3.2)" + ], + "metadata": { + "id": "lJf33AMIq1v7" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 1: Observation and Instrumentation" + ], + "metadata": { + "id": "zNLCI7CZOnkB" + } + }, + { + "cell_type": "markdown", + "source": [ + "A university's admission is related to the different aspects of the society, and often becomes a good reflection on societal's values and dynamic. For this part of the assignment, we will be working with a segment of **UC Berkeley's 1973 graduate admission data** to further explore how gender (recorded binary: Female and Male during 1973) plays a role in admission." + ], + "metadata": { + "id": "MGDqpkk_1KTw" + } + }, + { + "cell_type": "markdown", + "source": [ + "First, let's run the data! Fill in the code to read *```berkeley.csv```* below to call the data." + ], + "metadata": { + "id": "YSAXX8QL2xlT" + } + }, + { + "cell_type": "code", + "source": [ + "# Sync Google Drive to get CSV file\n", + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ], + "metadata": { + "id": "_VawtGjD3Owl", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "eda4615e-a12e-4267-e352-a84ecd7d502e" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Load UC Berkeley 1973 Graduate Admission Rate Dataset\n", + "berkeley = ...\n", + "berkeley.head(15)" + ], + "metadata": { + "id": "Vnswi18I7i8W", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 176 + }, + "outputId": "2d148baa-b534-4a75-dc72-8a57c61726d4" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "error", + "ename": "AttributeError", + "evalue": "'ellipsis' object has no attribute 'head'", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Load UC Berkeley 1973 Graduate Admission Rate Dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mberkeley\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mberkeley\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: 'ellipsis' object has no attribute 'head'" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Instructor_only/ Solution\n", + "\n", + "berkeley = pd.read_csv('/content/drive/MyDrive/SSS/berkeley.csv')\n", + "berkeley.head(15)" + ], + "metadata": { + "id": "HOhh5H9V3bAA", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 519 + }, + "outputId": "2aacdcff-0d9d-4fcd-dba8-2145737f40ca" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Year Major Gender Admission\n", + "0 1973 C F Rejected\n", + "1 1973 B M Accepted\n", + "2 1973 Other F Accepted\n", + "3 1973 Other M Accepted\n", + "4 1973 Other M Rejected\n", + "5 1973 Other M Rejected\n", + "6 1973 F F Accepted\n", + "7 1973 Other M Accepted\n", + "8 1973 Other M Rejected\n", + "9 1973 A M Accepted\n", + "10 1973 Other F Rejected\n", + "11 1973 B M Accepted\n", + "12 1973 C M Rejected\n", + "13 1973 A M Rejected\n", + "14 1973 Other M Rejected" + ], + "text/html": [ + "\n", + "
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YearMajorGenderAdmission
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61973FFAccepted
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121973CMRejected
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\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "berkeley", + "summary": "{\n \"name\": \"berkeley\",\n \"rows\": 12763,\n \"fields\": [\n {\n \"column\": \"Year\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 1973,\n \"max\": 1973,\n \"num_unique_values\": 1,\n \"samples\": [\n 1973\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Major\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 7,\n \"samples\": [\n \"C\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Gender\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"M\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Admission\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"Accepted\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 17 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "**Question 1.1 (short answer):** Just at first glance, what can you observe from the data? What assumptions can you make based on this data and your prior knowledge of the background on admission or your classroom experience at UC Berkeley?\n", + "\n", + "***If you do not have any prior knowledge, explain your assumptions.*" + ], + "metadata": { + "id": "Q_mv8xpDOul4" + } + }, + { + "cell_type": "markdown", + "source": [ + "_Your Answer Here_" + ], + "metadata": { + "id": "i0oqhiE20lJH" + } + }, + { + "cell_type": "code", + "source": [ + "# Instructor_Only / Solution\n", + "# Student's answer may vary. Example response below.\n", + "\n", + "# At first glance, there seem to be more male admitted to the UC Berkeley graduate programs. I assume that male are more likely to be admitted, as I observe male as a majority in the classrooms." + ], + "metadata": { + "id": "i4Z2mDPV0p9E" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "" + ], + "metadata": { + "id": "9pAbvE2y98bS" + } + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "**Question 1.2 (short answer):** Do you believe in your senses regarding this topic? Why or why not? Provide a credence level (i.e. \"I am 70% confident that..\") for your assumptions above." + ], + "metadata": { + "id": "bUZKf7D4AW26" + } + }, + { + "cell_type": "markdown", + "source": [ + "_Your Answer Here_" + ], + "metadata": { + "id": "qbSuW8ipCao3" + } + }, + { + "cell_type": "code", + "source": [ + "# Learning goal:\n", + "# 3.2) Calibration of Credence Levels\n", + "## Appreciate that one can improve on the calibration of their credence levels, and one should strive to reach an accurate calibration." + ], + "metadata": { + "id": "orLwdSln_St-" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Instructor_Only / Solution\n", + "# Student's answer may vary. Example response below.\n", + "\n", + "# I am 45% confident about my claim above. don't particularly believe in my senses because I might be exposed to only certain environment (DS class) and therefore have a skewed view of gender ratio in class/ admission." + ], + "metadata": { + "id": "KwWq9sYv-kfe" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "" + ], + "metadata": { + "id": "OpHCtk5OCjd2" + } + }, + { + "cell_type": "markdown", + "source": [ + "Our senses can be limiting or not possible in some aspect. Now, let's utilize ```pandas```, ```numpy```, and ```matplotlib```\n", + "\n", + "\n", + " instruments to explore this dataset!" + ], + "metadata": { + "id": "QavTevaj_hJg" + } + }, + { + "cell_type": "code", + "source": [ + "# Data Collection and Trustworthiness:\n", + "# Explore the origins of the data (e.g., Berkeley dataset), its collection process, and its validity.\n", + "# Discuss factors that could affect data collection and responses.\n", + "# Consider watching an associated video to deepen understanding.\n" + ], + "metadata": { + "id": "rdHV8ck5_A0y" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Calculate the total number of females and males\n", + "total_f = (berkeley[\"Gender\"] == \"F\").sum()\n", + "total_m = (berkeley[\"Gender\"] == \"M\").sum()\n", + "\n", + "# Calculate the number of accepted females and males\n", + "accepted_f = berkeley[(berkeley[\"Admission\"] == \"Accepted\") & (berkeley[\"Gender\"] == \"F\")].shape[0]\n", + "accepted_m = berkeley[(berkeley[\"Admission\"] == \"Accepted\") & (berkeley[\"Gender\"] == \"M\")].shape[0]\n", + "\n", + "# Calculate the acceptance rates for females and males\n", + "acceptance_rate_f = (accepted_f / total_f) * 100\n", + "acceptance_rate_m = (accepted_m / total_m) * 100\n", + "\n", + "# Print the results\n", + "print(f\"1973's Berkeley admission rate seems to be: female: {acceptance_rate_f}% and male: {acceptance_rate_m}%\")" + ], + "metadata": { + "id": "5SVFiSno0_5B", + "outputId": "13c9b19d-c285-4bd7-a001-d3e771812f29", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1973's Berkeley admission rate seems to be: female: 34.57532978477204% and male: 44.27860696517413%\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Create a pivot table to get the totals for Accepted and Rejected admissions per Gender\n", + "admission_f_m = berkeley\n", + "admission_f_m = pd.pivot_table(admission_f_m, index = \"Gender\", columns = \"Admission\", aggfunc=\"size\")\n", + "admission_f_m = admission_f_m.reset_index()\n", + "admission_f_m[\"Gender\"] = [\"Female\", \"Male\"]\n", + "admission_f_m" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 112 + }, + "id": "-IrbelUX43C1", + "outputId": "b52d68e6-c42a-4bc6-faad-0198ed7b2601" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Admission Gender Accepted Rejected\n", + "0 Female 1494 2827\n", + "1 Male 3738 4704" + ], + "text/html": [ + "\n", + "
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AdmissionGenderAcceptedRejected
0Female14942827
1Male37384704
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AdmissionGenderAcceptedRejectedAcceptance Rate
0Female149428270.345753
1Male373847040.442786
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\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "admission_f_m", + "summary": "{\n \"name\": \"admission_f_m\",\n \"rows\": 2,\n \"fields\": [\n {\n \"column\": \"Gender\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"Male\",\n \"Female\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Accepted\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1586,\n \"min\": 1494,\n \"max\": 3738,\n \"num_unique_values\": 2,\n \"samples\": [\n 3738,\n 1494\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Rejected\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1327,\n \"min\": 2827,\n \"max\": 4704,\n \"num_unique_values\": 2,\n \"samples\": [\n 4704,\n 2827\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Acceptance Rate\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.06861253093994998,\n \"min\": 0.3457532978477204,\n \"max\": 0.4427860696517413,\n \"num_unique_values\": 2,\n \"samples\": [\n 0.4427860696517413,\n 0.3457532978477204\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 13 + } + ] + }, + { + "cell_type": "code", + "source": [ + "plt.barh(admission_f_m[\"Gender\"], admission_f_m[\"Acceptance Rate\"])\n", + "plt.title(\"Acceptance Rates by Gender\")\n", + "plt.xlabel(\"Acceptance Rate\")\n", + "plt.ylabel(\"Gender\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 490 + }, + "id": "rlSwjLm65Ztv", + "outputId": "2f1a5ef3-07e0-434d-971d-921e3a15339c" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0, 0.5, 'Gender')" + ] + }, + "metadata": {}, + "execution_count": 14 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "id": "cXzOYZzsLdqH" + } + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "MlxvLcfa6WUy" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "CTXXklQkGkaE" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "AWdkn60VGvRB" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Takeaway\n", + "In this lab, we aim to prepare students for utilizing data science and decision making skillsets with the UC Berkeley 1973 Graduate Admission\n", + "Rate dataset. The objectives of this lab are as follows:\n", + "\n", + "\n", + "- **Senses and Instrumentation**:\n", + " - Learn how science uses senses and instruments for observation.\n", + " - Trust instruments for precise observations where direct senses can't reach.\n", + " - Understand instrument validation and the extension of objective reality through their use.\n", + "\n", + "- **Probabilistic Reasoning**:\n", + " - Understand the importance of confidence in judgments and decision-making under uncertainty.\n", + " - Recognize inherent uncertainties in claims and the value of scientific expressions of uncertainty.\n", + "\n", + "- **Confirmation Bias**:\n", + " - Be aware of the tendency to favor existing beliefs, even against evidence.\n", + " - Learn about selective exposure and biased assimilation, and how to mitigate confirmation bias by seeking counter-evidence.\n", + "\n", + "- **When Is Science Suspect**:\n", + " - Acknowledge the misuse of science in reinforcing power structures.\n", + " - Be cautious of science in studies involving human groups and aware of social dynamics affecting scientific assessments." + ], + "metadata": { + "id": "Yhk-Ws727IN_" + } + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "P37NKrIvG0kM" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "M6wWfOr8G68M" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "hG9UdCU4HQrW" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "d4DAKHOPHYGJ" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "VNxLvdJnHYsi" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# INSTRUCTOR ONLY: SPLIT 2 (9.1, 10.1)" + ], + "metadata": { + "id": "oRzwEUm8rIrv" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 2 Exploratory Data Analysis (EDA) and Initial Observations\n" + ], + "metadata": { + "id": "g_Tv_Spb8CX6" + } + }, + { + "cell_type": "markdown", + "source": [ + "EDA is the process of analyzing/summarizing data to extract valuable insights and patterns that can help guide further analysis.\n", + "\n", + "EDA is usually performed at the beginning of a data science project and helps to guide the direction of the analysis. EDA allows us to gain an understanding of the data, identify any patterns or anomalies, and detect any potential issues that may affect the analysis.\n", + "\n", + "In the following problems, we will perform EDA on our admission rates dataset." + ], + "metadata": { + "id": "Vtq2dEA78WVl" + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "id": "0aeRGwattZIu" + } + }, + { + "cell_type": "markdown", + "source": [ + "**Question #**: Load the dataset..." + ], + "metadata": { + "id": "ukHvLizB-R1C" + } + }, + { + "cell_type": "code", + "source": [ + "#SOLUTION (note that the path to the dataset will be local for students, so it'll be \"path/berkeley.csv\")\n", + "berkeley = pd.read_csv('/content/drive/MyDrive/SSS/berkeley.csv')\n", + "berkeley.head(10)\n", + "\n", + "# YOUR CODE HERE" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 + }, + "id": "dEZUOpwM-Rgd", + "outputId": "c3674b7a-b1c8-4c3a-ee7d-9b4f154b6e6d" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Year Major Gender Admission\n", + "0 1973 C F Rejected\n", + "1 1973 B M Accepted\n", + "2 1973 Other F Accepted\n", + "3 1973 Other M Accepted\n", + "4 1973 Other M Rejected\n", + "5 1973 Other M Rejected\n", + "6 1973 F F Accepted\n", + "7 1973 Other M Accepted\n", + "8 1973 Other M Rejected\n", + "9 1973 A M Accepted" + ], + "text/html": [ + "\n", + "
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Scroll through it, and make some initial observations. What trends do you notice? (1-2 Sentences)" + ], + "metadata": { + "id": "73pfixNb93-k" + } + }, + { + "cell_type": "markdown", + "source": [ + "SOLUTION: We want students to make some initial observations. They may note the features in the dataset, disproportionate applications from males to females, etc.\n", + "\n", + "*Your answer here*" + ], + "metadata": { + "id": "m8uLv9sX_H6f" + } + }, + { + "cell_type": "code", + "source": [ + "# Learning goal:\n", + "# 1.2) Shared reality and modelling\n", + "## Appreciate that one can improve on the calibration of their credence levels, and one should strive to reach an accurate calibration." + ], + "metadata": { + "id": "ki80gc-LR4uC" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Instructor_Only / Solution\n", + "# Student's answer may vary. Example response below.\n", + "\n", + "# ...." + ], + "metadata": { + "id": "nNY_kVQaRsuY" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Question # (Short Answer:** Given the observations you made, make some claim about the dataset. What is the credence level for your claim?" + ], + "metadata": { + "id": "0uERtWeMF-MK" + } + }, + { + "cell_type": "markdown", + "source": [ + "*Your answer here*" + ], + "metadata": { + "id": "7FJJ9RX2GOnA" + } + }, + { + "cell_type": "code", + "source": [ + "# Learning goal:\n", + "# 3.2) Calibration of Credence Levels\n", + "## Appreciate that one can improve on the calibration of their credence levels, and one should strive to reach an accurate calibration." + ], + "metadata": { + "id": "DRigxEOsSJ5F" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Instructor_Only / Solution\n", + "# Student's answer may vary. Example response below.\n", + "\n", + "# ...." + ], + "metadata": { + "id": "1zNmh-1gRtU6" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Question #**: Using the `berkeley` dataframe, create a dictionary of the following form:\n", + "\n", + "{\"M\" : # of male applicants accepted, \"F\" : # of female applicants accepted}" + ], + "metadata": { + "id": "cJxsp7qnii3Q" + } + }, + { + "cell_type": "code", + "source": [ + "# SOLUTION\n", + "berkeley_accepted = berkeley[berkeley[\"Admission\"] == \"Accepted\"]\n", + "m_accepted = berkeley_accepted[berkeley_accepted[\"Gender\"] == \"M\"].shape[0]\n", + "f_accepted = berkeley_accepted[berkeley_accepted[\"Gender\"] == \"F\"].shape[0]\n", + "\n", + "dict_accepted = {\"M\" : m_accepted, \"F\" : f_accepted}\n", + "dict_accepted" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "CeuZRKMDihhc", + "outputId": "ce89b4c8-f2d0-4c01-859b-1f9c6f77616f" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'M': 3738, 'F': 1494}" + ] + }, + "metadata": {}, + "execution_count": 20 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Question #**: Utilizing the dictionary you made above, create a bar plot comparing the number of accepted students by gender." + ], + "metadata": { + "id": "lY9bZlwf9qA9" + } + }, + { + "cell_type": "code", + "source": [ + "# SOLUTION\n", + "plt.bar(x=dict_accepted.keys(), height=dict_accepted.values())" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 448 + }, + "id": "S4EuumaMhFXq", + "outputId": "3e168a98-7650-4bbe-f3f8-22a71a7d565a" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 24 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Question #**:" + ], + "metadata": { + "id": "M3Sgb-tlg2vE" + } + }, + { + "cell_type": "markdown", + "source": [ + "# INSTRUCTOR ONLY: SPLIT 3 (11.2)" + ], + "metadata": { + "id": "unEy-czwrNEk" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 3: Understanding Visualizations and Further Implications" + ], + "metadata": { + "id": "OzqB193ktnPN" + } + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "fClac_Pn9pXS" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "ORRnkrZs8Vv6" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From 73c518b0096198653f96a898fcba7b587e555c6f Mon Sep 17 00:00:00 2001 From: Su Min Park <92177904+michellesuminpark@users.noreply.github.com> Date: Thu, 21 Mar 2024 13:58:17 -0700 Subject: [PATCH 2/5] Created using Colaboratory --- .../admission_rate/admission_rate_part1.ipynb | 2068 +++++++++++++++++ 1 file changed, 2068 insertions(+) create mode 100644 2024/admission_rate/admission_rate_part1.ipynb diff --git a/2024/admission_rate/admission_rate_part1.ipynb b/2024/admission_rate/admission_rate_part1.ipynb new file mode 100644 index 0000000..5e4f638 --- /dev/null +++ b/2024/admission_rate/admission_rate_part1.ipynb @@ -0,0 +1,2068 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **[LS22] UC Berkeley Admission Rate**" + ], + "metadata": { + "id": "naFAkdOL7PXO" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "d5r5TaunzrFo" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "source": [ + "
\n", + "\n", + "
" + ], + "metadata": { + "id": "r9dIdDaj-Y0K" + } + }, + { + "cell_type": "markdown", + "source": [ + "# INSTRUCTOR ONLY: SPLIT 1 (1.2, 2.1, 2.2, 3.1, 3.2)" + ], + "metadata": { + "id": "lJf33AMIq1v7" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 1: Observation and Instrumentation" + ], + "metadata": { + "id": "zNLCI7CZOnkB" + } + }, + { + "cell_type": "markdown", + "source": [ + "A university's admission is related to the different aspects of the society, and often becomes a good reflection on societal's values and dynamic. For this part of the assignment, we will be working with a segment of **UC Berkeley's 1973 graduate admission data** to further explore how gender (recorded binary: Female and Male during 1973) plays a role in admission." + ], + "metadata": { + "id": "MGDqpkk_1KTw" + } + }, + { + "cell_type": "markdown", + "source": [ + "First, let's run the data! Fill in the code to read *```berkeley.csv```* below to call the data." + ], + "metadata": { + "id": "YSAXX8QL2xlT" + } + }, + { + "cell_type": "code", + "source": [ + "# Sync Google Drive to get CSV file\n", + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ], + "metadata": { + "id": "_VawtGjD3Owl", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "eda4615e-a12e-4267-e352-a84ecd7d502e" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Load UC Berkeley 1973 Graduate Admission Rate Dataset\n", + "berkeley = ...\n", + "berkeley.head(15)" + ], + "metadata": { + "id": "Vnswi18I7i8W", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 176 + }, + "outputId": "2d148baa-b534-4a75-dc72-8a57c61726d4" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "error", + "ename": "AttributeError", + "evalue": "'ellipsis' object has no attribute 'head'", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Load UC Berkeley 1973 Graduate Admission Rate Dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mberkeley\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mberkeley\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: 'ellipsis' object has no attribute 'head'" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Instructor_only/ Solution\n", + "\n", + "berkeley = pd.read_csv('/content/drive/MyDrive/SSS/berkeley.csv')\n", + "berkeley.head(15)" + ], + "metadata": { + "id": "HOhh5H9V3bAA", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 519 + }, + "outputId": "2aacdcff-0d9d-4fcd-dba8-2145737f40ca" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Year Major Gender Admission\n", + "0 1973 C F Rejected\n", + "1 1973 B M Accepted\n", + "2 1973 Other F Accepted\n", + "3 1973 Other M Accepted\n", + "4 1973 Other M Rejected\n", + "5 1973 Other M Rejected\n", + "6 1973 F F Accepted\n", + "7 1973 Other M Accepted\n", + "8 1973 Other M Rejected\n", + "9 1973 A M Accepted\n", + "10 1973 Other F Rejected\n", + "11 1973 B M Accepted\n", + "12 1973 C M Rejected\n", + "13 1973 A M Rejected\n", + "14 1973 Other M Rejected" + ], + "text/html": [ + "\n", + "
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YearMajorGenderAdmission
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51973OtherMRejected
61973FFAccepted
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\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "berkeley", + "summary": "{\n \"name\": \"berkeley\",\n \"rows\": 12763,\n \"fields\": [\n {\n \"column\": \"Year\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 1973,\n \"max\": 1973,\n \"num_unique_values\": 1,\n \"samples\": [\n 1973\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Major\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 7,\n \"samples\": [\n \"C\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Gender\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"M\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Admission\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"Accepted\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 17 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "**Question 1.1 (short answer):** Just at first glance, what can you observe from the data? What assumptions can you make based on this data and your prior knowledge of the background on admission or your classroom experience at UC Berkeley?\n", + "\n", + "***If you do not have any prior knowledge, explain your assumptions.*" + ], + "metadata": { + "id": "Q_mv8xpDOul4" + } + }, + { + "cell_type": "markdown", + "source": [ + "_Your Answer Here_" + ], + "metadata": { + "id": "i0oqhiE20lJH" + } + }, + { + "cell_type": "code", + "source": [ + "# Instructor_Only / Solution\n", + "# Student's answer may vary. Example response below.\n", + "\n", + "# At first glance, there seem to be more male admitted to the UC Berkeley graduate programs. I assume that male are more likely to be admitted, as I observe male as a majority in the classrooms." + ], + "metadata": { + "id": "i4Z2mDPV0p9E" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "" + ], + "metadata": { + "id": "9pAbvE2y98bS" + } + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "**Question 1.2 (short answer):** Do you believe in your senses regarding this topic? Why or why not? Provide a credence level (i.e. \"I am 70% confident that..\") for your assumptions above." + ], + "metadata": { + "id": "bUZKf7D4AW26" + } + }, + { + "cell_type": "markdown", + "source": [ + "_Your Answer Here_" + ], + "metadata": { + "id": "qbSuW8ipCao3" + } + }, + { + "cell_type": "code", + "source": [ + "# Learning goal:\n", + "# 3.2) Calibration of Credence Levels\n", + "## Appreciate that one can improve on the calibration of their credence levels, and one should strive to reach an accurate calibration." + ], + "metadata": { + "id": "orLwdSln_St-" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Instructor_Only / Solution\n", + "# Student's answer may vary. Example response below.\n", + "\n", + "# I am 45% confident about my claim above. don't particularly believe in my senses because I might be exposed to only certain environment (DS class) and therefore have a skewed view of gender ratio in class/ admission." + ], + "metadata": { + "id": "KwWq9sYv-kfe" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "" + ], + "metadata": { + "id": "OpHCtk5OCjd2" + } + }, + { + "cell_type": "markdown", + "source": [ + "Our senses can be limiting or not possible in some aspect. Now, let's utilize ```pandas```, ```numpy```, and ```matplotlib```\n", + "\n", + "\n", + " instruments to explore this dataset!" + ], + "metadata": { + "id": "QavTevaj_hJg" + } + }, + { + "cell_type": "code", + "source": [ + "# Data Collection and Trustworthiness:\n", + "# Explore the origins of the data (e.g., Berkeley dataset), its collection process, and its validity.\n", + "# Discuss factors that could affect data collection and responses.\n", + "# Consider watching an associated video to deepen understanding.\n" + ], + "metadata": { + "id": "rdHV8ck5_A0y" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Calculate the total number of females and males\n", + "total_f = (berkeley[\"Gender\"] == \"F\").sum()\n", + "total_m = (berkeley[\"Gender\"] == \"M\").sum()\n", + "\n", + "# Calculate the number of accepted females and males\n", + "accepted_f = berkeley[(berkeley[\"Admission\"] == \"Accepted\") & (berkeley[\"Gender\"] == \"F\")].shape[0]\n", + "accepted_m = berkeley[(berkeley[\"Admission\"] == \"Accepted\") & (berkeley[\"Gender\"] == \"M\")].shape[0]\n", + "\n", + "# Calculate the acceptance rates for females and males\n", + "acceptance_rate_f = (accepted_f / total_f) * 100\n", + "acceptance_rate_m = (accepted_m / total_m) * 100\n", + "\n", + "# Print the results\n", + "print(f\"1973's Berkeley admission rate seems to be: female: {acceptance_rate_f}% and male: {acceptance_rate_m}%\")" + ], + "metadata": { + "id": "5SVFiSno0_5B", + "outputId": "13c9b19d-c285-4bd7-a001-d3e771812f29", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1973's Berkeley admission rate seems to be: female: 34.57532978477204% and male: 44.27860696517413%\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Create a pivot table to get the totals for Accepted and Rejected admissions per Gender\n", + "admission_f_m = berkeley\n", + "admission_f_m = pd.pivot_table(admission_f_m, index = \"Gender\", columns = \"Admission\", aggfunc=\"size\")\n", + "admission_f_m = admission_f_m.reset_index()\n", + "admission_f_m[\"Gender\"] = [\"Female\", \"Male\"]\n", + "admission_f_m" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 112 + }, + "id": "-IrbelUX43C1", + "outputId": "b52d68e6-c42a-4bc6-faad-0198ed7b2601" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Admission Gender Accepted Rejected\n", + "0 Female 1494 2827\n", + "1 Male 3738 4704" + ], + "text/html": [ + "\n", + "
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AdmissionGenderAcceptedRejected
0Female14942827
1Male37384704
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\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "admission_f_m", + "summary": "{\n \"name\": \"admission_f_m\",\n \"rows\": 2,\n \"fields\": [\n {\n \"column\": \"Gender\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"Male\",\n \"Female\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Accepted\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1586,\n \"min\": 1494,\n \"max\": 3738,\n \"num_unique_values\": 2,\n \"samples\": [\n 3738,\n 1494\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Rejected\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1327,\n \"min\": 2827,\n \"max\": 4704,\n \"num_unique_values\": 2,\n \"samples\": [\n 4704,\n 2827\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 12 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Calculate acceptance rates\n", + "admission_f_m[\"Acceptance Rate\"] = admission_f_m[\"Accepted\"] / (admission_f_m[\"Accepted\"] + admission_f_m[\"Rejected\"])\n", + "\n", + "# The DataFrame admission_f_m now includes the acceptance rates\n", + "admission_f_m\n" + ], + "metadata": { + "id": "aP61TndW2AXY", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 112 + }, + "outputId": "11211bb2-a636-4093-ab61-cd5bfda3938d" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Admission Gender Accepted Rejected Acceptance Rate\n", + "0 Female 1494 2827 0.345753\n", + "1 Male 3738 4704 0.442786" + ], + "text/html": [ + "\n", + "
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AdmissionGenderAcceptedRejectedAcceptance Rate
0Female149428270.345753
1Male373847040.442786
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\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "admission_f_m", + "summary": "{\n \"name\": \"admission_f_m\",\n \"rows\": 2,\n \"fields\": [\n {\n \"column\": \"Gender\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"Male\",\n \"Female\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Accepted\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1586,\n \"min\": 1494,\n \"max\": 3738,\n \"num_unique_values\": 2,\n \"samples\": [\n 3738,\n 1494\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Rejected\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1327,\n \"min\": 2827,\n \"max\": 4704,\n \"num_unique_values\": 2,\n \"samples\": [\n 4704,\n 2827\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Acceptance Rate\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.06861253093994998,\n \"min\": 0.3457532978477204,\n \"max\": 0.4427860696517413,\n \"num_unique_values\": 2,\n \"samples\": [\n 0.4427860696517413,\n 0.3457532978477204\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 13 + } + ] + }, + { + "cell_type": "code", + "source": [ + "plt.barh(admission_f_m[\"Gender\"], admission_f_m[\"Acceptance Rate\"])\n", + "plt.title(\"Acceptance Rates by Gender\")\n", + "plt.xlabel(\"Acceptance Rate\")\n", + "plt.ylabel(\"Gender\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 490 + }, + "id": "rlSwjLm65Ztv", + "outputId": "2f1a5ef3-07e0-434d-971d-921e3a15339c" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0, 0.5, 'Gender')" + ] + }, + "metadata": {}, + "execution_count": 14 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "id": "cXzOYZzsLdqH" + } + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "MlxvLcfa6WUy" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "CTXXklQkGkaE" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "AWdkn60VGvRB" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Takeaway\n", + "In this lab, we aim to prepare students for utilizing data science and decision making skillsets with the UC Berkeley 1973 Graduate Admission\n", + "Rate dataset. The objectives of this lab are as follows:\n", + "\n", + "\n", + "- **Senses and Instrumentation**:\n", + " - Learn how science uses senses and instruments for observation.\n", + " - Trust instruments for precise observations where direct senses can't reach.\n", + " - Understand instrument validation and the extension of objective reality through their use.\n", + "\n", + "- **Probabilistic Reasoning**:\n", + " - Understand the importance of confidence in judgments and decision-making under uncertainty.\n", + " - Recognize inherent uncertainties in claims and the value of scientific expressions of uncertainty.\n", + "\n", + "- **Confirmation Bias**:\n", + " - Be aware of the tendency to favor existing beliefs, even against evidence.\n", + " - Learn about selective exposure and biased assimilation, and how to mitigate confirmation bias by seeking counter-evidence.\n", + "\n", + "- **When Is Science Suspect**:\n", + " - Acknowledge the misuse of science in reinforcing power structures.\n", + " - Be cautious of science in studies involving human groups and aware of social dynamics affecting scientific assessments." + ], + "metadata": { + "id": "Yhk-Ws727IN_" + } + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "P37NKrIvG0kM" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "M6wWfOr8G68M" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "hG9UdCU4HQrW" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "d4DAKHOPHYGJ" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "VNxLvdJnHYsi" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# INSTRUCTOR ONLY: SPLIT 2 (9.1, 10.1)" + ], + "metadata": { + "id": "oRzwEUm8rIrv" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 2 Exploratory Data Analysis (EDA) and Initial Observations\n" + ], + "metadata": { + "id": "g_Tv_Spb8CX6" + } + }, + { + "cell_type": "markdown", + "source": [ + "EDA is the process of analyzing/summarizing data to extract valuable insights and patterns that can help guide further analysis.\n", + "\n", + "EDA is usually performed at the beginning of a data science project and helps to guide the direction of the analysis. EDA allows us to gain an understanding of the data, identify any patterns or anomalies, and detect any potential issues that may affect the analysis.\n", + "\n", + "In the following problems, we will perform EDA on our admission rates dataset." + ], + "metadata": { + "id": "Vtq2dEA78WVl" + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "id": "0aeRGwattZIu" + } + }, + { + "cell_type": "markdown", + "source": [ + "**Question #**: Load the dataset..." + ], + "metadata": { + "id": "ukHvLizB-R1C" + } + }, + { + "cell_type": "code", + "source": [ + "#SOLUTION (note that the path to the dataset will be local for students, so it'll be \"path/berkeley.csv\")\n", + "berkeley = pd.read_csv('/content/drive/MyDrive/SSS/berkeley.csv')\n", + "berkeley.head(10)\n", + "\n", + "# YOUR CODE HERE" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 + }, + "id": "dEZUOpwM-Rgd", + "outputId": "c3674b7a-b1c8-4c3a-ee7d-9b4f154b6e6d" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Year Major Gender Admission\n", + "0 1973 C F Rejected\n", + "1 1973 B M Accepted\n", + "2 1973 Other F Accepted\n", + "3 1973 Other M Accepted\n", + "4 1973 Other M Rejected\n", + "5 1973 Other M Rejected\n", + "6 1973 F F Accepted\n", + "7 1973 Other M Accepted\n", + "8 1973 Other M Rejected\n", + "9 1973 A M Accepted" + ], + "text/html": [ + "\n", + "
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Scroll through it, and make some initial observations. What trends do you notice? (1-2 Sentences)" + ], + "metadata": { + "id": "73pfixNb93-k" + } + }, + { + "cell_type": "markdown", + "source": [ + "SOLUTION: We want students to make some initial observations. They may note the features in the dataset, disproportionate applications from males to females, etc.\n", + "\n", + "*Your answer here*" + ], + "metadata": { + "id": "m8uLv9sX_H6f" + } + }, + { + "cell_type": "code", + "source": [ + "# Learning goal:\n", + "# 1.2) Shared reality and modelling\n", + "## Appreciate that one can improve on the calibration of their credence levels, and one should strive to reach an accurate calibration." + ], + "metadata": { + "id": "ki80gc-LR4uC" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Instructor_Only / Solution\n", + "# Student's answer may vary. Example response below.\n", + "\n", + "# ...." + ], + "metadata": { + "id": "nNY_kVQaRsuY" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Question # (Short Answer:** Given the observations you made, make some claim about the dataset. What is the credence level for your claim?" + ], + "metadata": { + "id": "0uERtWeMF-MK" + } + }, + { + "cell_type": "markdown", + "source": [ + "*Your answer here*" + ], + "metadata": { + "id": "7FJJ9RX2GOnA" + } + }, + { + "cell_type": "code", + "source": [ + "# Learning goal:\n", + "# 3.2) Calibration of Credence Levels\n", + "## Appreciate that one can improve on the calibration of their credence levels, and one should strive to reach an accurate calibration." + ], + "metadata": { + "id": "DRigxEOsSJ5F" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Instructor_Only / Solution\n", + "# Student's answer may vary. Example response below.\n", + "\n", + "# ...." + ], + "metadata": { + "id": "1zNmh-1gRtU6" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Question #**: Using the `berkeley` dataframe, create a dictionary of the following form:\n", + "\n", + "{\"M\" : # of male applicants accepted, \"F\" : # of female applicants accepted}" + ], + "metadata": { + "id": "cJxsp7qnii3Q" + } + }, + { + "cell_type": "code", + "source": [ + "# SOLUTION\n", + "berkeley_accepted = berkeley[berkeley[\"Admission\"] == \"Accepted\"]\n", + "m_accepted = berkeley_accepted[berkeley_accepted[\"Gender\"] == \"M\"].shape[0]\n", + "f_accepted = berkeley_accepted[berkeley_accepted[\"Gender\"] == \"F\"].shape[0]\n", + "\n", + "dict_accepted = {\"M\" : m_accepted, \"F\" : f_accepted}\n", + "dict_accepted" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "CeuZRKMDihhc", + "outputId": "ce89b4c8-f2d0-4c01-859b-1f9c6f77616f" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'M': 3738, 'F': 1494}" + ] + }, + "metadata": {}, + "execution_count": 20 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Question #**: Utilizing the dictionary you made above, create a bar plot comparing the number of accepted students by gender." + ], + "metadata": { + "id": "lY9bZlwf9qA9" + } + }, + { + "cell_type": "code", + "source": [ + "# SOLUTION\n", + "plt.bar(x=dict_accepted.keys(), height=dict_accepted.values())" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 448 + }, + "id": "S4EuumaMhFXq", + "outputId": "3e168a98-7650-4bbe-f3f8-22a71a7d565a" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 24 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Question #**:" + ], + "metadata": { + "id": "M3Sgb-tlg2vE" + } + }, + { + "cell_type": "markdown", + "source": [ + "# INSTRUCTOR ONLY: SPLIT 3 (11.2)" + ], + "metadata": { + "id": "unEy-czwrNEk" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 3: Understanding Visualizations and Further Implications" + ], + "metadata": { + "id": "OzqB193ktnPN" + } + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "fClac_Pn9pXS" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "ORRnkrZs8Vv6" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From 60b03f679b80df66ffa3ffdcb42a4955776183a5 Mon Sep 17 00:00:00 2001 From: Su Min Park <92177904+michellesuminpark@users.noreply.github.com> Date: Thu, 21 Mar 2024 14:07:54 -0700 Subject: [PATCH 3/5] Created using Colaboratory --- .../admission_rate/admission_rate_part3.ipynb | 2068 +++++++++++++++++ 1 file changed, 2068 insertions(+) create mode 100644 2024/admission_rate/admission_rate_part3.ipynb diff --git a/2024/admission_rate/admission_rate_part3.ipynb b/2024/admission_rate/admission_rate_part3.ipynb new file mode 100644 index 0000000..a5a7053 --- /dev/null +++ b/2024/admission_rate/admission_rate_part3.ipynb @@ -0,0 +1,2068 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **[LS22] UC Berkeley Admission Rate**" + ], + "metadata": { + "id": "naFAkdOL7PXO" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "d5r5TaunzrFo" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "source": [ + "
\n", + "\n", + "
" + ], + "metadata": { + "id": "r9dIdDaj-Y0K" + } + }, + { + "cell_type": "markdown", + "source": [ + "# INSTRUCTOR ONLY: SPLIT 1 (1.2, 2.1, 2.2, 3.1, 3.2)" + ], + "metadata": { + "id": "lJf33AMIq1v7" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 1: Observation and Instrumentation" + ], + "metadata": { + "id": "zNLCI7CZOnkB" + } + }, + { + "cell_type": "markdown", + "source": [ + "A university's admission is related to the different aspects of the society, and often becomes a good reflection on societal's values and dynamic. For this part of the assignment, we will be working with a segment of **UC Berkeley's 1973 graduate admission data** to further explore how gender (recorded binary: Female and Male during 1973) plays a role in admission." + ], + "metadata": { + "id": "MGDqpkk_1KTw" + } + }, + { + "cell_type": "markdown", + "source": [ + "First, let's run the data! Fill in the code to read *```berkeley.csv```* below to call the data." + ], + "metadata": { + "id": "YSAXX8QL2xlT" + } + }, + { + "cell_type": "code", + "source": [ + "# Sync Google Drive to get CSV file\n", + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ], + "metadata": { + "id": "_VawtGjD3Owl", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "eda4615e-a12e-4267-e352-a84ecd7d502e" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Load UC Berkeley 1973 Graduate Admission Rate Dataset\n", + "berkeley = ...\n", + "berkeley.head(15)" + ], + "metadata": { + "id": "Vnswi18I7i8W", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 176 + }, + "outputId": "2d148baa-b534-4a75-dc72-8a57c61726d4" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "error", + "ename": "AttributeError", + "evalue": "'ellipsis' object has no attribute 'head'", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Load UC Berkeley 1973 Graduate Admission Rate Dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mberkeley\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mberkeley\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: 'ellipsis' object has no attribute 'head'" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Instructor_only/ Solution\n", + "\n", + "berkeley = pd.read_csv('/content/drive/MyDrive/SSS/berkeley.csv')\n", + "berkeley.head(15)" + ], + "metadata": { + "id": "HOhh5H9V3bAA", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 519 + }, + "outputId": "2aacdcff-0d9d-4fcd-dba8-2145737f40ca" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Year Major Gender Admission\n", + "0 1973 C F Rejected\n", + "1 1973 B M Accepted\n", + "2 1973 Other F Accepted\n", + "3 1973 Other M Accepted\n", + "4 1973 Other M Rejected\n", + "5 1973 Other M Rejected\n", + "6 1973 F F Accepted\n", + "7 1973 Other M Accepted\n", + "8 1973 Other M Rejected\n", + "9 1973 A M Accepted\n", + "10 1973 Other F Rejected\n", + "11 1973 B M Accepted\n", + "12 1973 C M Rejected\n", + "13 1973 A M Rejected\n", + "14 1973 Other M Rejected" + ], + "text/html": [ + "\n", + "
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YearMajorGenderAdmission
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51973OtherMRejected
61973FFAccepted
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\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "berkeley", + "summary": "{\n \"name\": \"berkeley\",\n \"rows\": 12763,\n \"fields\": [\n {\n \"column\": \"Year\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 1973,\n \"max\": 1973,\n \"num_unique_values\": 1,\n \"samples\": [\n 1973\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Major\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 7,\n \"samples\": [\n \"C\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Gender\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"M\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Admission\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"Accepted\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 17 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "**Question 1.1 (short answer):** Just at first glance, what can you observe from the data? What assumptions can you make based on this data and your prior knowledge of the background on admission or your classroom experience at UC Berkeley?\n", + "\n", + "***If you do not have any prior knowledge, explain your assumptions.*" + ], + "metadata": { + "id": "Q_mv8xpDOul4" + } + }, + { + "cell_type": "markdown", + "source": [ + "_Your Answer Here_" + ], + "metadata": { + "id": "i0oqhiE20lJH" + } + }, + { + "cell_type": "code", + "source": [ + "# Instructor_Only / Solution\n", + "# Student's answer may vary. Example response below.\n", + "\n", + "# At first glance, there seem to be more male admitted to the UC Berkeley graduate programs. I assume that male are more likely to be admitted, as I observe male as a majority in the classrooms." + ], + "metadata": { + "id": "i4Z2mDPV0p9E" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "" + ], + "metadata": { + "id": "9pAbvE2y98bS" + } + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "**Question 1.2 (short answer):** Do you believe in your senses regarding this topic? Why or why not? Provide a credence level (i.e. \"I am 70% confident that..\") for your assumptions above." + ], + "metadata": { + "id": "bUZKf7D4AW26" + } + }, + { + "cell_type": "markdown", + "source": [ + "_Your Answer Here_" + ], + "metadata": { + "id": "qbSuW8ipCao3" + } + }, + { + "cell_type": "code", + "source": [ + "# Learning goal:\n", + "# 3.2) Calibration of Credence Levels\n", + "## Appreciate that one can improve on the calibration of their credence levels, and one should strive to reach an accurate calibration." + ], + "metadata": { + "id": "orLwdSln_St-" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Instructor_Only / Solution\n", + "# Student's answer may vary. Example response below.\n", + "\n", + "# I am 45% confident about my claim above. don't particularly believe in my senses because I might be exposed to only certain environment (DS class) and therefore have a skewed view of gender ratio in class/ admission." + ], + "metadata": { + "id": "KwWq9sYv-kfe" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "" + ], + "metadata": { + "id": "OpHCtk5OCjd2" + } + }, + { + "cell_type": "markdown", + "source": [ + "Our senses can be limiting or not possible in some aspect. Now, let's utilize ```pandas```, ```numpy```, and ```matplotlib```\n", + "\n", + "\n", + " instruments to explore this dataset!" + ], + "metadata": { + "id": "QavTevaj_hJg" + } + }, + { + "cell_type": "code", + "source": [ + "# Data Collection and Trustworthiness:\n", + "# Explore the origins of the data (e.g., Berkeley dataset), its collection process, and its validity.\n", + "# Discuss factors that could affect data collection and responses.\n", + "# Consider watching an associated video to deepen understanding.\n" + ], + "metadata": { + "id": "rdHV8ck5_A0y" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Calculate the total number of females and males\n", + "total_f = (berkeley[\"Gender\"] == \"F\").sum()\n", + "total_m = (berkeley[\"Gender\"] == \"M\").sum()\n", + "\n", + "# Calculate the number of accepted females and males\n", + "accepted_f = berkeley[(berkeley[\"Admission\"] == \"Accepted\") & (berkeley[\"Gender\"] == \"F\")].shape[0]\n", + "accepted_m = berkeley[(berkeley[\"Admission\"] == \"Accepted\") & (berkeley[\"Gender\"] == \"M\")].shape[0]\n", + "\n", + "# Calculate the acceptance rates for females and males\n", + "acceptance_rate_f = (accepted_f / total_f) * 100\n", + "acceptance_rate_m = (accepted_m / total_m) * 100\n", + "\n", + "# Print the results\n", + "print(f\"1973's Berkeley admission rate seems to be: female: {acceptance_rate_f}% and male: {acceptance_rate_m}%\")" + ], + "metadata": { + "id": "5SVFiSno0_5B", + "outputId": "13c9b19d-c285-4bd7-a001-d3e771812f29", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1973's Berkeley admission rate seems to be: female: 34.57532978477204% and male: 44.27860696517413%\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Create a pivot table to get the totals for Accepted and Rejected admissions per Gender\n", + "admission_f_m = berkeley\n", + "admission_f_m = pd.pivot_table(admission_f_m, index = \"Gender\", columns = \"Admission\", aggfunc=\"size\")\n", + "admission_f_m = admission_f_m.reset_index()\n", + "admission_f_m[\"Gender\"] = [\"Female\", \"Male\"]\n", + "admission_f_m" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 112 + }, + "id": "-IrbelUX43C1", + "outputId": "b52d68e6-c42a-4bc6-faad-0198ed7b2601" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Admission Gender Accepted Rejected\n", + "0 Female 1494 2827\n", + "1 Male 3738 4704" + ], + "text/html": [ + "\n", + "
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AdmissionGenderAcceptedRejected
0Female14942827
1Male37384704
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\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "admission_f_m", + "summary": "{\n \"name\": \"admission_f_m\",\n \"rows\": 2,\n \"fields\": [\n {\n \"column\": \"Gender\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"Male\",\n \"Female\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Accepted\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1586,\n \"min\": 1494,\n \"max\": 3738,\n \"num_unique_values\": 2,\n \"samples\": [\n 3738,\n 1494\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Rejected\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1327,\n \"min\": 2827,\n \"max\": 4704,\n \"num_unique_values\": 2,\n \"samples\": [\n 4704,\n 2827\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 12 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Calculate acceptance rates\n", + "admission_f_m[\"Acceptance Rate\"] = admission_f_m[\"Accepted\"] / (admission_f_m[\"Accepted\"] + admission_f_m[\"Rejected\"])\n", + "\n", + "# The DataFrame admission_f_m now includes the acceptance rates\n", + "admission_f_m\n" + ], + "metadata": { + "id": "aP61TndW2AXY", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 112 + }, + "outputId": "11211bb2-a636-4093-ab61-cd5bfda3938d" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Admission Gender Accepted Rejected Acceptance Rate\n", + "0 Female 1494 2827 0.345753\n", + "1 Male 3738 4704 0.442786" + ], + "text/html": [ + "\n", + "
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AdmissionGenderAcceptedRejectedAcceptance Rate
0Female149428270.345753
1Male373847040.442786
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\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "admission_f_m", + "summary": "{\n \"name\": \"admission_f_m\",\n \"rows\": 2,\n \"fields\": [\n {\n \"column\": \"Gender\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"Male\",\n \"Female\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Accepted\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1586,\n \"min\": 1494,\n \"max\": 3738,\n \"num_unique_values\": 2,\n \"samples\": [\n 3738,\n 1494\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Rejected\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1327,\n \"min\": 2827,\n \"max\": 4704,\n \"num_unique_values\": 2,\n \"samples\": [\n 4704,\n 2827\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Acceptance Rate\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.06861253093994998,\n \"min\": 0.3457532978477204,\n \"max\": 0.4427860696517413,\n \"num_unique_values\": 2,\n \"samples\": [\n 0.4427860696517413,\n 0.3457532978477204\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 13 + } + ] + }, + { + "cell_type": "code", + "source": [ + "plt.barh(admission_f_m[\"Gender\"], admission_f_m[\"Acceptance Rate\"])\n", + "plt.title(\"Acceptance Rates by Gender\")\n", + "plt.xlabel(\"Acceptance Rate\")\n", + "plt.ylabel(\"Gender\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 490 + }, + "id": "rlSwjLm65Ztv", + "outputId": "2f1a5ef3-07e0-434d-971d-921e3a15339c" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0, 0.5, 'Gender')" + ] + }, + "metadata": {}, + "execution_count": 14 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "id": "cXzOYZzsLdqH" + } + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "MlxvLcfa6WUy" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "CTXXklQkGkaE" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "AWdkn60VGvRB" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Takeaway\n", + "In this lab, we aim to prepare students for utilizing data science and decision making skillsets with the UC Berkeley 1973 Graduate Admission\n", + "Rate dataset. The objectives of this lab are as follows:\n", + "\n", + "\n", + "- **Senses and Instrumentation**:\n", + " - Learn how science uses senses and instruments for observation.\n", + " - Trust instruments for precise observations where direct senses can't reach.\n", + " - Understand instrument validation and the extension of objective reality through their use.\n", + "\n", + "- **Probabilistic Reasoning**:\n", + " - Understand the importance of confidence in judgments and decision-making under uncertainty.\n", + " - Recognize inherent uncertainties in claims and the value of scientific expressions of uncertainty.\n", + "\n", + "- **Confirmation Bias**:\n", + " - Be aware of the tendency to favor existing beliefs, even against evidence.\n", + " - Learn about selective exposure and biased assimilation, and how to mitigate confirmation bias by seeking counter-evidence.\n", + "\n", + "- **When Is Science Suspect**:\n", + " - Acknowledge the misuse of science in reinforcing power structures.\n", + " - Be cautious of science in studies involving human groups and aware of social dynamics affecting scientific assessments." + ], + "metadata": { + "id": "Yhk-Ws727IN_" + } + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "P37NKrIvG0kM" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "M6wWfOr8G68M" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "hG9UdCU4HQrW" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "d4DAKHOPHYGJ" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "VNxLvdJnHYsi" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# INSTRUCTOR ONLY: SPLIT 2 (9.1, 10.1)" + ], + "metadata": { + "id": "oRzwEUm8rIrv" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 2 Exploratory Data Analysis (EDA) and Initial Observations\n" + ], + "metadata": { + "id": "g_Tv_Spb8CX6" + } + }, + { + "cell_type": "markdown", + "source": [ + "EDA is the process of analyzing/summarizing data to extract valuable insights and patterns that can help guide further analysis.\n", + "\n", + "EDA is usually performed at the beginning of a data science project and helps to guide the direction of the analysis. EDA allows us to gain an understanding of the data, identify any patterns or anomalies, and detect any potential issues that may affect the analysis.\n", + "\n", + "In the following problems, we will perform EDA on our admission rates dataset." + ], + "metadata": { + "id": "Vtq2dEA78WVl" + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "id": "0aeRGwattZIu" + } + }, + { + "cell_type": "markdown", + "source": [ + "**Question #**: Load the dataset..." + ], + "metadata": { + "id": "ukHvLizB-R1C" + } + }, + { + "cell_type": "code", + "source": [ + "#SOLUTION (note that the path to the dataset will be local for students, so it'll be \"path/berkeley.csv\")\n", + "berkeley = pd.read_csv('/content/drive/MyDrive/SSS/berkeley.csv')\n", + "berkeley.head(10)\n", + "\n", + "# YOUR CODE HERE" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 363 + }, + "id": "dEZUOpwM-Rgd", + "outputId": "c3674b7a-b1c8-4c3a-ee7d-9b4f154b6e6d" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Year Major Gender Admission\n", + "0 1973 C F Rejected\n", + "1 1973 B M Accepted\n", + "2 1973 Other F Accepted\n", + "3 1973 Other M Accepted\n", + "4 1973 Other M Rejected\n", + "5 1973 Other M Rejected\n", + "6 1973 F F Accepted\n", + "7 1973 Other M Accepted\n", + "8 1973 Other M Rejected\n", + "9 1973 A M Accepted" + ], + "text/html": [ + "\n", + "
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Scroll through it, and make some initial observations. What trends do you notice? (1-2 Sentences)" + ], + "metadata": { + "id": "73pfixNb93-k" + } + }, + { + "cell_type": "markdown", + "source": [ + "SOLUTION: We want students to make some initial observations. They may note the features in the dataset, disproportionate applications from males to females, etc.\n", + "\n", + "*Your answer here*" + ], + "metadata": { + "id": "m8uLv9sX_H6f" + } + }, + { + "cell_type": "code", + "source": [ + "# Learning goal:\n", + "# 1.2) Shared reality and modelling\n", + "## Appreciate that one can improve on the calibration of their credence levels, and one should strive to reach an accurate calibration." + ], + "metadata": { + "id": "ki80gc-LR4uC" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Instructor_Only / Solution\n", + "# Student's answer may vary. Example response below.\n", + "\n", + "# ...." + ], + "metadata": { + "id": "nNY_kVQaRsuY" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Question # (Short Answer:** Given the observations you made, make some claim about the dataset. What is the credence level for your claim?" + ], + "metadata": { + "id": "0uERtWeMF-MK" + } + }, + { + "cell_type": "markdown", + "source": [ + "*Your answer here*" + ], + "metadata": { + "id": "7FJJ9RX2GOnA" + } + }, + { + "cell_type": "code", + "source": [ + "# Learning goal:\n", + "# 3.2) Calibration of Credence Levels\n", + "## Appreciate that one can improve on the calibration of their credence levels, and one should strive to reach an accurate calibration." + ], + "metadata": { + "id": "DRigxEOsSJ5F" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Instructor_Only / Solution\n", + "# Student's answer may vary. Example response below.\n", + "\n", + "# ...." + ], + "metadata": { + "id": "1zNmh-1gRtU6" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Question #**: Using the `berkeley` dataframe, create a dictionary of the following form:\n", + "\n", + "{\"M\" : # of male applicants accepted, \"F\" : # of female applicants accepted}" + ], + "metadata": { + "id": "cJxsp7qnii3Q" + } + }, + { + "cell_type": "code", + "source": [ + "# SOLUTION\n", + "berkeley_accepted = berkeley[berkeley[\"Admission\"] == \"Accepted\"]\n", + "m_accepted = berkeley_accepted[berkeley_accepted[\"Gender\"] == \"M\"].shape[0]\n", + "f_accepted = berkeley_accepted[berkeley_accepted[\"Gender\"] == \"F\"].shape[0]\n", + "\n", + "dict_accepted = {\"M\" : m_accepted, \"F\" : f_accepted}\n", + "dict_accepted" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "CeuZRKMDihhc", + "outputId": "ce89b4c8-f2d0-4c01-859b-1f9c6f77616f" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'M': 3738, 'F': 1494}" + ] + }, + "metadata": {}, + "execution_count": 20 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Question #**: Utilizing the dictionary you made above, create a bar plot comparing the number of accepted students by gender." + ], + "metadata": { + "id": "lY9bZlwf9qA9" + } + }, + { + "cell_type": "code", + "source": [ + "# SOLUTION\n", + "plt.bar(x=dict_accepted.keys(), height=dict_accepted.values())" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 448 + }, + "id": "S4EuumaMhFXq", + "outputId": "3e168a98-7650-4bbe-f3f8-22a71a7d565a" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 24 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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+ }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Question #**:" + ], + "metadata": { + "id": "M3Sgb-tlg2vE" + } + }, + { + "cell_type": "markdown", + "source": [ + "# INSTRUCTOR ONLY: SPLIT 3 (11.2)" + ], + "metadata": { + "id": "unEy-czwrNEk" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 3: Understanding Visualizations and Further Implications" + ], + "metadata": { + "id": "OzqB193ktnPN" + } + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "fClac_Pn9pXS" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "ORRnkrZs8Vv6" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From d992e7c7349aafb170d2a80d260f01c2ae4e3259 Mon Sep 17 00:00:00 2001 From: Su Min Park <92177904+michellesuminpark@users.noreply.github.com> Date: Thu, 21 Mar 2024 14:11:38 -0700 Subject: [PATCH 4/5] Changed the python to only have part 1 --- .../admission_rate/admission_rate_part1.ipynb | 1400 +---------------- 1 file changed, 19 insertions(+), 1381 deletions(-) diff --git a/2024/admission_rate/admission_rate_part1.ipynb b/2024/admission_rate/admission_rate_part1.ipynb index 5e4f638..2fbf9b1 100644 --- a/2024/admission_rate/admission_rate_part1.ipynb +++ b/2024/admission_rate/admission_rate_part1.ipynb @@ -655,1402 +655,40 @@ "id": "QavTevaj_hJg" } }, - { - "cell_type": "code", - "source": [ - "# Data Collection and Trustworthiness:\n", - "# Explore the origins of the data (e.g., Berkeley dataset), its collection process, and its validity.\n", - "# Discuss factors that could affect data collection and responses.\n", - "# Consider watching an associated video to deepen understanding.\n" - ], - "metadata": { - "id": "rdHV8ck5_A0y" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Calculate the total number of females and males\n", - "total_f = (berkeley[\"Gender\"] == \"F\").sum()\n", - "total_m = (berkeley[\"Gender\"] == \"M\").sum()\n", - "\n", - "# Calculate the number of accepted females and males\n", - "accepted_f = berkeley[(berkeley[\"Admission\"] == \"Accepted\") & (berkeley[\"Gender\"] == \"F\")].shape[0]\n", - "accepted_m = berkeley[(berkeley[\"Admission\"] == \"Accepted\") & (berkeley[\"Gender\"] == \"M\")].shape[0]\n", - "\n", - "# Calculate the acceptance rates for females and males\n", - "acceptance_rate_f = (accepted_f / total_f) * 100\n", - "acceptance_rate_m = (accepted_m / total_m) * 100\n", - "\n", - "# Print the results\n", - "print(f\"1973's Berkeley admission rate seems to be: female: {acceptance_rate_f}% and male: {acceptance_rate_m}%\")" - ], - "metadata": { - "id": "5SVFiSno0_5B", - "outputId": "13c9b19d-c285-4bd7-a001-d3e771812f29", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "1973's Berkeley admission rate seems to be: female: 34.57532978477204% and male: 44.27860696517413%\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "# Create a pivot table to get the totals for Accepted and Rejected admissions per Gender\n", - "admission_f_m = berkeley\n", - "admission_f_m = pd.pivot_table(admission_f_m, index = \"Gender\", columns = \"Admission\", aggfunc=\"size\")\n", - "admission_f_m = admission_f_m.reset_index()\n", - "admission_f_m[\"Gender\"] = [\"Female\", \"Male\"]\n", - "admission_f_m" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 112 - }, - "id": "-IrbelUX43C1", - "outputId": "b52d68e6-c42a-4bc6-faad-0198ed7b2601" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "Admission Gender Accepted Rejected\n", - "0 Female 1494 2827\n", - "1 Male 3738 4704" - ], - "text/html": [ - "\n", - "
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AdmissionGenderAcceptedRejected
0Female14942827
1Male37384704
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AdmissionGenderAcceptedRejectedAcceptance Rate
0Female149428270.345753
1Male373847040.442786
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\n" - ], - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "dataframe", - "variable_name": "admission_f_m", - "summary": "{\n \"name\": \"admission_f_m\",\n \"rows\": 2,\n \"fields\": [\n {\n \"column\": \"Gender\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"Male\",\n \"Female\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Accepted\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1586,\n \"min\": 1494,\n \"max\": 3738,\n \"num_unique_values\": 2,\n \"samples\": [\n 3738,\n 1494\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Rejected\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1327,\n \"min\": 2827,\n \"max\": 4704,\n \"num_unique_values\": 2,\n \"samples\": [\n 4704,\n 2827\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Acceptance Rate\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.06861253093994998,\n \"min\": 0.3457532978477204,\n \"max\": 0.4427860696517413,\n \"num_unique_values\": 2,\n \"samples\": [\n 0.4427860696517413,\n 0.3457532978477204\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" - } - }, - "metadata": {}, - "execution_count": 13 - } - ] - }, - { - "cell_type": "code", - "source": [ - "plt.barh(admission_f_m[\"Gender\"], admission_f_m[\"Acceptance Rate\"])\n", - "plt.title(\"Acceptance Rates by Gender\")\n", - "plt.xlabel(\"Acceptance Rate\")\n", - "plt.ylabel(\"Gender\")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 490 - }, - "id": "rlSwjLm65Ztv", - "outputId": "2f1a5ef3-07e0-434d-971d-921e3a15339c" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "Text(0, 0.5, 'Gender')" - ] - }, - "metadata": {}, - "execution_count": 14 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "id": "cXzOYZzsLdqH" - } - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "MlxvLcfa6WUy" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "CTXXklQkGkaE" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "AWdkn60VGvRB" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "## Takeaway\n", - "In this lab, we aim to prepare students for utilizing data science and decision making skillsets with the UC Berkeley 1973 Graduate Admission\n", - "Rate dataset. The objectives of this lab are as follows:\n", - "\n", - "\n", - "- **Senses and Instrumentation**:\n", - " - Learn how science uses senses and instruments for observation.\n", - " - Trust instruments for precise observations where direct senses can't reach.\n", - " - Understand instrument validation and the extension of objective reality through their use.\n", - "\n", - "- **Probabilistic Reasoning**:\n", - " - Understand the importance of confidence in judgments and decision-making under uncertainty.\n", - " - Recognize inherent uncertainties in claims and the value of scientific expressions of uncertainty.\n", - "\n", - "- **Confirmation Bias**:\n", - " - Be aware of the tendency to favor existing beliefs, even against evidence.\n", - " - Learn about selective exposure and biased assimilation, and how to mitigate confirmation bias by seeking counter-evidence.\n", - "\n", - "- **When Is Science Suspect**:\n", - " - Acknowledge the misuse of science in reinforcing power structures.\n", - " - Be cautious of science in studies involving human groups and aware of social dynamics affecting scientific assessments." - ], - "metadata": { - "id": "Yhk-Ws727IN_" - } - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "P37NKrIvG0kM" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "M6wWfOr8G68M" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "hG9UdCU4HQrW" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "d4DAKHOPHYGJ" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "VNxLvdJnHYsi" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "# INSTRUCTOR ONLY: SPLIT 2 (9.1, 10.1)" - ], - "metadata": { - "id": "oRzwEUm8rIrv" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 2 Exploratory Data Analysis (EDA) and Initial Observations\n" - ], - "metadata": { - "id": "g_Tv_Spb8CX6" - } - }, - { - "cell_type": "markdown", - "source": [ - "EDA is the process of analyzing/summarizing data to extract valuable insights and patterns that can help guide further analysis.\n", - "\n", - "EDA is usually performed at the beginning of a data science project and helps to guide the direction of the analysis. EDA allows us to gain an understanding of the data, identify any patterns or anomalies, and detect any potential issues that may affect the analysis.\n", - "\n", - "In the following problems, we will perform EDA on our admission rates dataset." - ], - "metadata": { - "id": "Vtq2dEA78WVl" - } - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "id": "0aeRGwattZIu" - } - }, - { - "cell_type": "markdown", - "source": [ - "**Question #**: Load the dataset..." - ], - "metadata": { - "id": "ukHvLizB-R1C" - } - }, - { - "cell_type": "code", - "source": [ - "#SOLUTION (note that the path to the dataset will be local for students, so it'll be \"path/berkeley.csv\")\n", - "berkeley = pd.read_csv('/content/drive/MyDrive/SSS/berkeley.csv')\n", - "berkeley.head(10)\n", - "\n", - "# YOUR CODE HERE" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 363 - }, - "id": "dEZUOpwM-Rgd", - "outputId": "c3674b7a-b1c8-4c3a-ee7d-9b4f154b6e6d" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " Year Major Gender Admission\n", - "0 1973 C F Rejected\n", - "1 1973 B M Accepted\n", - "2 1973 Other F Accepted\n", - "3 1973 Other M Accepted\n", - "4 1973 Other M Rejected\n", - "5 1973 Other M Rejected\n", - "6 1973 F F Accepted\n", - "7 1973 Other M Accepted\n", - "8 1973 Other M Rejected\n", - "9 1973 A M Accepted" - ], - "text/html": [ - "\n", - "
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Scroll through it, and make some initial observations. What trends do you notice? (1-2 Sentences)" - ], - "metadata": { - "id": "73pfixNb93-k" - } - }, { "cell_type": "markdown", "source": [ - "SOLUTION: We want students to make some initial observations. They may note the features in the dataset, disproportionate applications from males to females, etc.\n", + "## Takeaway\n", + "In this lab, we aim to prepare students for utilizing data science and decision making skillsets with the UC Berkeley 1973 Graduate Admission\n", + "Rate dataset. The objectives of this lab are as follows:\n", "\n", - "*Your answer here*" - ], - "metadata": { - "id": "m8uLv9sX_H6f" - } - }, - { - "cell_type": "code", - "source": [ - "# Learning goal:\n", - "# 1.2) Shared reality and modelling\n", - "## Appreciate that one can improve on the calibration of their credence levels, and one should strive to reach an accurate calibration." - ], - "metadata": { - "id": "ki80gc-LR4uC" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Instructor_Only / Solution\n", - "# Student's answer may vary. Example response below.\n", "\n", - "# ...." - ], - "metadata": { - "id": "nNY_kVQaRsuY" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "**Question # (Short Answer:** Given the observations you made, make some claim about the dataset. What is the credence level for your claim?" - ], - "metadata": { - "id": "0uERtWeMF-MK" - } - }, - { - "cell_type": "markdown", - "source": [ - "*Your answer here*" - ], - "metadata": { - "id": "7FJJ9RX2GOnA" - } - }, - { - "cell_type": "code", - "source": [ - "# Learning goal:\n", - "# 3.2) Calibration of Credence Levels\n", - "## Appreciate that one can improve on the calibration of their credence levels, and one should strive to reach an accurate calibration." - ], - "metadata": { - "id": "DRigxEOsSJ5F" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Instructor_Only / Solution\n", - "# Student's answer may vary. Example response below.\n", + "- **Senses and Instrumentation**:\n", + " - Learn how science uses senses and instruments for observation.\n", + " - Trust instruments for precise observations where direct senses can't reach.\n", + " - Understand instrument validation and the extension of objective reality through their use.\n", "\n", - "# ...." - ], - "metadata": { - "id": "1zNmh-1gRtU6" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "**Question #**: Using the `berkeley` dataframe, create a dictionary of the following form:\n", + "- **Probabilistic Reasoning**:\n", + " - Understand the importance of confidence in judgments and decision-making under uncertainty.\n", + " - Recognize inherent uncertainties in claims and the value of scientific expressions of uncertainty.\n", "\n", - "{\"M\" : # of male applicants accepted, \"F\" : # of female applicants accepted}" - ], - "metadata": { - "id": "cJxsp7qnii3Q" - } - }, - { - "cell_type": "code", - "source": [ - "# SOLUTION\n", - "berkeley_accepted = berkeley[berkeley[\"Admission\"] == \"Accepted\"]\n", - "m_accepted = berkeley_accepted[berkeley_accepted[\"Gender\"] == \"M\"].shape[0]\n", - "f_accepted = berkeley_accepted[berkeley_accepted[\"Gender\"] == \"F\"].shape[0]\n", + "- **Confirmation Bias**:\n", + " - Be aware of the tendency to favor existing beliefs, even against evidence.\n", + " - Learn about selective exposure and biased assimilation, and how to mitigate confirmation bias by seeking counter-evidence.\n", "\n", - "dict_accepted = {\"M\" : m_accepted, \"F\" : f_accepted}\n", - "dict_accepted" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "CeuZRKMDihhc", - "outputId": "ce89b4c8-f2d0-4c01-859b-1f9c6f77616f" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "{'M': 3738, 'F': 1494}" - ] - }, - "metadata": {}, - "execution_count": 20 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "**Question #**: Utilizing the dictionary you made above, create a bar plot comparing the number of accepted students by gender." - ], - "metadata": { - "id": "lY9bZlwf9qA9" - } - }, - { - "cell_type": "code", - "source": [ - "# SOLUTION\n", - "plt.bar(x=dict_accepted.keys(), height=dict_accepted.values())" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 448 - }, - "id": "S4EuumaMhFXq", - "outputId": "3e168a98-7650-4bbe-f3f8-22a71a7d565a" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 24 - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "**Question #**:" - ], - "metadata": { - "id": "M3Sgb-tlg2vE" - } - }, - { - "cell_type": "markdown", - "source": [ - "# INSTRUCTOR ONLY: SPLIT 3 (11.2)" - ], - "metadata": { - "id": "unEy-czwrNEk" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Part 3: Understanding Visualizations and Further Implications" + "- **When Is Science Suspect**:\n", + " - Acknowledge the misuse of science in reinforcing power structures.\n", + " - Be cautious of science in studies involving human groups and aware of social dynamics affecting scientific assessments." ], "metadata": { - "id": "OzqB193ktnPN" + "id": "Yhk-Ws727IN_" } }, { "cell_type": "code", "source": [], "metadata": { - "id": "fClac_Pn9pXS" + "id": "P37NKrIvG0kM" }, "execution_count": null, "outputs": [] @@ -2059,7 +697,7 @@ "cell_type": "code", "source": [], "metadata": { - "id": "ORRnkrZs8Vv6" + "id": "M6wWfOr8G68M" }, "execution_count": null, "outputs": [] From eea3dc9b1f5173f8c4d82d4793815ab27345e9c6 Mon Sep 17 00:00:00 2001 From: Su Min Park <92177904+michellesuminpark@users.noreply.github.com> Date: Thu, 21 Mar 2024 14:22:32 -0700 Subject: [PATCH 5/5] Created using Colaboratory --- 2024/admission_rate/admission_rate.ipynb | 1323 ++++++++++++++++++---- 1 file changed, 1087 insertions(+), 236 deletions(-) diff --git a/2024/admission_rate/admission_rate.ipynb b/2024/admission_rate/admission_rate.ipynb index 22b0ab8..72cc2c3 100644 --- a/2024/admission_rate/admission_rate.ipynb +++ b/2024/admission_rate/admission_rate.ipynb @@ -1,14 +1,39 @@ { + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, "cells": [ { "cell_type": "markdown", "metadata": { - "id": "naFAkdOL7PXO" + "id": "view-in-github", + "colab_type": "text" }, "source": [ - "# **[LS22] Title of the lab**" + "\"Open" ] }, + { + "cell_type": "markdown", + "source": [ + "# **[LS22] UC Berkeley Admission Rate**" + ], + "metadata": { + "id": "naFAkdOL7PXO" + } + }, { "cell_type": "code", "execution_count": null, @@ -24,91 +49,148 @@ }, { "cell_type": "markdown", - "metadata": { - "id": "Yhk-Ws727IN_" - }, "source": [ - "# Introduction\n", - "In this lab, we aim to prepare students for utilizing data science and decision making skillsets with the UC Berkeley 1973 Graduate Admission\n", - "Rate dataset. The objectives of this lab are as follows:\n", - "\n", - "\n", - "- **Senses and Instrumentation**:\n", - " - Learn how science uses senses and instruments for observation.\n", - " - Trust instruments for precise observations where direct senses can't reach.\n", - " - Understand instrument validation and the extension of objective reality through their use.\n", - "\n", - "- **Probabilistic Reasoning**:\n", - " - Understand the importance of confidence in judgments and decision-making under uncertainty.\n", - " - Recognize inherent uncertainties in claims and the value of scientific expressions of uncertainty.\n", - "\n", - "- **Confirmation Bias**:\n", - " - Be aware of the tendency to favor existing beliefs, even against evidence.\n", - " - Learn about selective exposure and biased assimilation, and how to mitigate confirmation bias by seeking counter-evidence.\n", + "
\n", "\n", - "- **When Is Science Suspect**:\n", - " - Acknowledge the misuse of science in reinforcing power structures.\n", - " - Be cautious of science in studies involving human groups and aware of social dynamics affecting scientific assessments." - ] + "
" + ], + "metadata": { + "id": "r9dIdDaj-Y0K" + } + }, + { + "cell_type": "markdown", + "source": [ + "# INSTRUCTOR ONLY: SPLIT 1 (1.2, 2.1, 2.2, 3.1, 3.2)" + ], + "metadata": { + "id": "lJf33AMIq1v7" + } }, { "cell_type": "markdown", + "source": [ + "## Part 1: Observation and Instrumentation" + ], "metadata": { - "id": "r9dIdDaj-Y0K" - }, + "id": "zNLCI7CZOnkB" + } + }, + { + "cell_type": "markdown", "source": [ - "
\n", - "\n", - "
" - ] + "A university's admission is related to the different aspects of the society, and often becomes a good reflection on societal's values and dynamic. For this part of the assignment, we will be working with a segment of **UC Berkeley's 1973 graduate admission data** to further explore how gender (recorded binary: Female and Male during 1973) plays a role in admission." + ], + "metadata": { + "id": "MGDqpkk_1KTw" + } + }, + { + "cell_type": "markdown", + "source": [ + "First, let's run the data! Fill in the code to read *```berkeley.csv```* below to call the data." + ], + "metadata": { + "id": "YSAXX8QL2xlT" + } }, { "cell_type": "code", - "execution_count": null, + "source": [ + "# Sync Google Drive to get CSV file\n", + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ], "metadata": { + "id": "_VawtGjD3Owl", "colab": { "base_uri": "https://localhost:8080/" }, - "id": "AYTcGtCy2kXR", - "outputId": "d1feee96-5d01-4a50-8baf-369d8a46b0c9" + "outputId": "eda4615e-a12e-4267-e352-a84ecd7d502e" }, + "execution_count": null, "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" ] } - ], - "source": [ - "# Sync Google Drive to get CSV file\n", - "from google.colab import drive\n", - "drive.mount('/content/drive')" ] }, { "cell_type": "code", + "source": [ + "# Load UC Berkeley 1973 Graduate Admission Rate Dataset\n", + "berkeley = ...\n", + "berkeley.head(15)" + ], + "metadata": { + "id": "Vnswi18I7i8W", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 176 + }, + "outputId": "2d148baa-b534-4a75-dc72-8a57c61726d4" + }, "execution_count": null, + "outputs": [ + { + "output_type": "error", + "ename": "AttributeError", + "evalue": "'ellipsis' object has no attribute 'head'", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Load UC Berkeley 1973 Graduate Admission Rate Dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mberkeley\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mberkeley\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: 'ellipsis' object has no attribute 'head'" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Instructor_only/ Solution\n", + "\n", + "berkeley = pd.read_csv('/content/drive/MyDrive/SSS/berkeley.csv')\n", + "berkeley.head(15)" + ], "metadata": { + "id": "HOhh5H9V3bAA", "colab": { "base_uri": "https://localhost:8080/", - "height": 363 + "height": 519 }, - "id": "Hygl3HBl0-lL", - "outputId": "33d6f55e-d948-4012-a61b-f6ae8d625813" + "outputId": "2aacdcff-0d9d-4fcd-dba8-2145737f40ca" }, + "execution_count": null, "outputs": [ { + "output_type": "execute_result", "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "summary": "{\n \"name\": \"berkeley\",\n \"rows\": 12763,\n \"fields\": [\n {\n \"column\": \"Year\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 1973,\n \"max\": 1973,\n \"num_unique_values\": 1,\n \"samples\": [\n 1973\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Major\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 7,\n \"samples\": [\n \"C\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Gender\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"M\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Admission\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"Accepted\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", - "type": "dataframe", - "variable_name": "berkeley" - }, + "text/plain": [ + " Year Major Gender Admission\n", + "0 1973 C F Rejected\n", + "1 1973 B M Accepted\n", + "2 1973 Other F Accepted\n", + "3 1973 Other M Accepted\n", + "4 1973 Other M Rejected\n", + "5 1973 Other M Rejected\n", + "6 1973 F F Accepted\n", + "7 1973 Other M Accepted\n", + "8 1973 Other M Rejected\n", + "9 1973 A M Accepted\n", + "10 1973 Other F Rejected\n", + "11 1973 B M Accepted\n", + "12 1973 C M Rejected\n", + "13 1973 A M Rejected\n", + "14 1973 Other M Rejected" + ], "text/html": [ "\n", - "
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Scroll through it, and make some initial observations. What trends do you notice? (1-2 Sentences)" + ], + "metadata": { + "id": "73pfixNb93-k" + } + }, + { + "cell_type": "markdown", + "source": [ + "SOLUTION: We want students to make some initial observations. They may note the features in the dataset, disproportionate applications from males to females, etc.\n", + "\n", + "*Your answer here*" + ], + "metadata": { + "id": "m8uLv9sX_H6f" + } + }, + { + "cell_type": "code", + "source": [ + "# Learning goal:\n", + "# 1.2) Shared reality and modelling\n", + "## Appreciate that one can improve on the calibration of their credence levels, and one should strive to reach an accurate calibration." + ], + "metadata": { + "id": "ki80gc-LR4uC" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Instructor_Only / Solution\n", + "# Student's answer may vary. Example response below.\n", + "\n", + "# ...." + ], + "metadata": { + "id": "nNY_kVQaRsuY" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Question # (Short Answer:** Given the observations you made, make some claim about the dataset. What is the credence level for your claim?" + ], + "metadata": { + "id": "0uERtWeMF-MK" + } + }, + { + "cell_type": "markdown", + "source": [ + "*Your answer here*" + ], + "metadata": { + "id": "7FJJ9RX2GOnA" + } + }, + { + "cell_type": "code", + "source": [ + "# Learning goal:\n", + "# 3.2) Calibration of Credence Levels\n", + "## Appreciate that one can improve on the calibration of their credence levels, and one should strive to reach an accurate calibration." + ], + "metadata": { + "id": "DRigxEOsSJ5F" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Instructor_Only / Solution\n", + "# Student's answer may vary. Example response below.\n", + "\n", + "# ...." + ], + "metadata": { + "id": "1zNmh-1gRtU6" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**Question #**: Using the `berkeley` dataframe, create a dictionary of the following form:\n", + "\n", + "{\"M\" : # of male applicants accepted, \"F\" : # of female applicants accepted}" + ], + "metadata": { + "id": "cJxsp7qnii3Q" + } + }, + { + "cell_type": "code", + "source": [ + "# SOLUTION\n", + "berkeley_accepted = berkeley[berkeley[\"Admission\"] == \"Accepted\"]\n", + "m_accepted = berkeley_accepted[berkeley_accepted[\"Gender\"] == \"M\"].shape[0]\n", + "f_accepted = berkeley_accepted[berkeley_accepted[\"Gender\"] == \"F\"].shape[0]\n", + "\n", + "dict_accepted = {\"M\" : m_accepted, \"F\" : f_accepted}\n", + "dict_accepted" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "CeuZRKMDihhc", + "outputId": "ce89b4c8-f2d0-4c01-859b-1f9c6f77616f" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'M': 3738, 'F': 1494}" + ] + }, + "metadata": {}, + "execution_count": 20 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Question #**: Utilizing the dictionary you made above, create a bar plot comparing the number of accepted students by gender." + ], + "metadata": { + "id": "lY9bZlwf9qA9" + } + }, + { + "cell_type": "code", + "source": [ + "# SOLUTION\n", + "plt.bar(x=dict_accepted.keys(), height=dict_accepted.values())" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 448 + }, + "id": "S4EuumaMhFXq", + "outputId": "3e168a98-7650-4bbe-f3f8-22a71a7d565a" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 24 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Question #**:" + ], + "metadata": { + "id": "M3Sgb-tlg2vE" + } + }, + { + "cell_type": "markdown", + "source": [ + "# INSTRUCTOR ONLY: SPLIT 3 (11.2)" + ], + "metadata": { + "id": "unEy-czwrNEk" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Part 3: Understanding Visualizations and Further Implications" + ], + "metadata": { + "id": "OzqB193ktnPN" + } + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "fClac_Pn9pXS" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "ORRnkrZs8Vv6" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file